Start with the data path
Document where data originates, what fields are needed, where processing occurs, who can access it and where outputs are stored. Do this before discussing model choice.
Separate experimentation from production
Proofs of concept can move quickly with synthetic data. Production systems need access control, monitoring, evaluation, fallback behavior and a plan for model or provider changes.
Define evaluation in business terms
Accuracy is not one universal number. A support classifier, extraction model and recommendation system fail in different ways. Create test sets that represent real cases and measure the errors that matter to the business.
Keep a human review path where risk is material
High-impact decisions, unusual cases and low-confidence outputs may need human verification. The workflow should make uncertainty visible rather than hiding it behind a confident interface.
Clarify ownership and model-provider terms
Contracts should distinguish business data, prompts, code, fine-tuning assets, evaluation sets and third-party model services. Confirm whether any input may be retained or used to improve an external provider’s models.
Track model and prompt changes
AI behavior can change when prompts, retrieval data, model versions or third-party APIs change. Treat those inputs as versioned system components and record what was evaluated before production release.
Budget for evaluation, not just generation
AI can produce output cheaply while making verification expensive. Include human review, benchmark creation, red-team testing and monitoring in the project budget so the buyer understands the true delivery cost.
Design fallback behavior
Production AI systems need a response when the model is unavailable, too slow or below confidence thresholds. Decide whether the workflow retries, returns a safe default, routes to a human or blocks the action. That behavior should be tested explicitly.
Monitor real-world drift
A model that performs well on a launch dataset can degrade as customer behavior or source data changes. Define ongoing sampling and review so quality problems are found before users become the monitoring system.
Keep a model inventory
Record which external models, versions and endpoints are used, what data they receive and which business workflows depend on them. This becomes important when a vendor changes pricing, terms, availability or model behavior. The buyer should know where substitution is possible and where revalidation is required.